TAILIEUCHUNG - Modeling Chaotic Behavior of Stock Indices Using Intelligent Paradigms

Though the resaw process can be easy and a delight when the machines are well adjusted and your stock has consistent grain, it can be a nightmare when either aren't. Resaw on the band saw requires that the guides be adjusted carefully. If you have only cut thinner stock on your band saw (up to 2" or so) you may have never had the need to carefully adjust the guides. Here it is critical so that the blade is guided in the same direction both below the table and above the work on the descending arm. Better quality replacement. | Modeling Chaotic Behavior of Stock Indices Using Intelligent Paradigms Ajith Abraham Ninan Sajith Philip1 and P. Saratchandran2 Department of Computer Science Oklahoma State University Tulsa Oklahoma 74106 USA Email department of Physics Cochin University of Science and Technology India Email nsp@ 2School of Electrical and Electronic Engineering Nanyang Technological University Singapore 639798 E-mail epsarat@ Abstract The use of intelligent systems for stock market predictions has been widely established. In this paper we investigate how the seemingly chaotic behavior of stock markets could be well represented using several connectionist paradigms and soft computing techniques. To demonstrate the different techniques we considered Nasdaq-100 index of Nasdaq Stock MarketSM and the S P CNX NIFTY stock index. We analyzed 7 year s Nasdaq 100 main index values and 4 year s NIFTY index values. This paper investigates the development of a reliable and efficient technique to model the seemingly chaotic behavior of stock markets. We considered an artificial neural network trained using Levenberg-Marquardt algorithm Support Vector Machine SVM Takagi-Sugeno neuro-fuzzy model and a Difference Boosting Neural Network DBNN . This paper briefly explains how the different connectionist paradigms could be formulated using different learning methods and then investigates whether they can provide the required level of performance which are sufficiently good and robust so as to provide a reliable forecast model for stock market indices. Experiment results reveal that all the connectionist paradigms considered could represent the stock indices behavior very accurately. Key words connectionist paradigm support vector machine neural network difference boosting neuro-fuzzy stock market. 1. INTRODUCTION Prediction of stocks is generally believed to be a very difficult task. The process behaves more like a random walk process and time .

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